Three Cases of Tickborne Francisella tularensis Infection, Austria, 2022.

Tularemia is increasing in Austria. We report Francisella tularensis subspecies holarctica isolated from 3 patients who had been bitten by arthropods. Next-generation sequencing showed substantial isolate similarity. Clinicians should consider bloodstream F. tularensis infections for patients with signs/symptoms of ulceroglandular tularemia, and surveillance of potential vectors should be intensified.

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Computational augmentation of neoplastic endometrial glands in digital pathology displays.

The pathologic diagnosis of neoplasia requires localization and classification of lesional tissue, a process that depends on the recognition of an abnormal spatial distribution of neoplastic elements relative to admixed normal background tissue. In endometrial intraepithelial neoplasia (EIN), a pre-cancer usually managed by hysterectomy, a clonally mutated proliferation of cytologically altered glands ('neoplastic-EIN') aggregates in clusters that also contain background non-neoplastic glands ('background-NL'). Here, we used image analysis to classify individual glands within endometrial tissue fragments as neoplastic-EIN or background-NL, and we used the distribution of predictions to localize foci diagnostic of EIN. Nuclear coordinates were automatically assigned and were used as vertices to generate Delaunay triangulations for each gland. Graph statistical variables were used to develop random forest algorithms to classify glands as neoplastic-EIN or background-NL. Individual glands in an independent validation set were scored by a 'ground truth' biomarker (PAX2 immunohistochemistry). We found that exclusion of small glands led to improvement in classification accuracy. Using an inclusion threshold of 200 nuclei per gland, our final model classification accuracy was 77.5% in the validation set, with a positive predictive value of 0.81. We leveraged this high positive predictive value in a point cloud overlay display to assist end-user identification of EIN foci. This study demonstrates that graph theory approaches applied to small-scale anatomic elements in the endometrium allow biologic classification by machine learning, and that spatial superimposition over large-scale tissue expanses can have practical diagnostic utility. We expect this augmented diagnostic approach to be generalizable to commonly encountered problems in other organ systems. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

[EMPAIA-ecosystem for pathology diagnostics with AI assistance]. / EMPAIA ­ Ökosystem zur Nutzung von KI in der Pathologie.

Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have already been established (analysis of prostate sections, ER, PR, and Her2 in breast cancer). Besides the still low use of virtual microscopy in practice, there are a number of hurdles that stand in the way of a rapid diffusion of AI applications. The EMPAIA project has a goal of removing these hurdles. The path taken to build an ecosystem for this purpose is intended to exemplify that the introduction of AI applications in image-based diagnostics is possible on a broad basis if the existing hurdles are removed in a joint, moderated process. The components of the EMPAIA ecosystem and its strategy will be described, and reference will be made to the technical solution approaches.

Zoonotic Pathogens in Ticks from Migratory Birds, Italy.

Migratory birds can transport infected ticks across continents. We evaluated pathogens in ticks collected from migratory birds in Italy. We found DNA from Rickettsia aeschlimannii, R. africae, and R. raoultii bacteria, all of which can cause disease in humans. Bird migrations might facilitate the spread of these pathogens into new areas.

Assessment of scalability and performance of the record linkage tool E-PIX® in managing multi-million patients in research projects at a large university hospital in Germany.

BACKGROUND: The identity management is a central component in medical research. Patients are recruited from various sites, which requires an error tolerant record linkage method, to ensure that patients are registered only once. In large research projects or institutions, the identity management has to deal with several thousands or millions of patients. In environments with large numbers of patients the register process could lead to high runtimes caused by record linkage. The Central Biomaterial Bank of the Charité (ZeBanC) searched for an identity management solution, which can handle millions of patients in large research projects with an acceptable performance. The goal of this paper was to simulate the registration of several million patients using the E-PIX service at Charité - Universitätsmedizin Berlin. The E-PIX service was evaluated in terms of needed runtimes, memory requirements, and processor utilization. A total of at least 20 million patients had to be registered. The runtimes to register patients into databases with various sizes should be examined, and the maximum number of patients, which the E-PIX service could handle, should be determined. METHODS: Tools were set up or developed to measure the needed runtimes, the memory used and the processor usage to register patients into various sizes of databases. To generate runtimes close to reality, modified patient data based on transposed real patient data were used for the simulation. The transposed patient data were sent to E-PIX to measure the runtimes of the registration process. This measurement was repeated for various database sizes. RESULTS: E-PIX is suitable to manage multi-million patients within a dataset. With the given hardware, it was possible to register a total of more than 30 million patients. It was possible to register more than 16 thousand patients per day into this database. CONCLUSIONS: The E-PIX tool fulfills the requirements of the Charité to be used for large research projects. The use of E-PIX is intended for the research context in the Charité.

Adult Hyalomma marginatum tick positive for Rickettsia aeschlimannii in Austria, October 2018.

We report on a non-indigenous adult Hyalomma marginatum tick in Austria carrying the human pathogenic Rickettsia aeschlimannii; presumably introduced as a nymph via migratory birds and completed the moulting within the same year. It was negative for Crimean-Congo haemorrhagic fever virus, but the finding of R. aeschlimannii represents a potential threat for humans due to its zoonotic character. Awareness of invasive tick species and carried pathogens should be improved in central and northern Europe.

Dermatomyositis With or Without Anti-Melanoma Differentiation-Associated Gene 5 Antibodies: Common Interferon Signature but Distinct NOS2 Expression.

The anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody is specifically associated with dermatomyositis (DM). Nevertheless, anti-MDA5(+)-patients experience characteristic symptoms distinct from classic DM, including severe signs of extramuscular involvement; however, the clinical signs of myopathy are mild or even absent. The morphological and immunological features are not yet described in adulthood. Data concerning the pathophysiology of anti-MDA5 DM are sparse; however, the importance of the interferon (IFN) type I pathway involved in DM has been shown. Our aim was to define morphological alterations of the skeletal muscle and the intrinsic immune response of anti-MDA5-positive DM patients. Immunohistological and RT-PCR analysis of muscle biopsy specimens from anti-MDA5 and classic DM were compared. Those with anti-MDA5 DM did not present the classic features of perifascicular fiber atrophy and major histocompatibility complex class I expression. They did not show significant signs of capillary loss; tubuloreticular formations were observed less frequently. Inflammation was focal, clustering around single vessels but significantly less intense. Expression of IFN-stimulated genes was up-regulated in anti-MDA5 DM; however, the IFN score was significantly lower. Characteristic features were observed in anti-MDA5 DM and not in classic DM patients. Only anti-MDA5 DM showed numerous nitric oxide synthase 2-positive muscle fibers with sarcoplasmic colocalization of markers of regeneration and cell stress. Anti-MDA5-positive patients demonstrate a morphological pattern distinct from classic DM.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Development of a Trusted Third Party at a Large University Hospital: Design and Implementation Study.

Background: Pseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring the direct identification of data to support various research processes while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units known as trusted third parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating adequate tool support. Common tasks of TTPs include supporting the secure registration and pseudonymization of patient and sample identities as well as managing consent. Objective: Despite the challenges involved, little has been published about successful architectures and functional tools for implementing TTPs in large university hospitals. The aim of this paper is to fill this research gap by describing the software architecture and tool set developed and deployed as part of a TTP established at Charité - Universitätsmedizin Berlin. Methods: The infrastructure for the TTP was designed to provide a modular structure while keeping maintenance requirements low. Basic functionalities were realized with the free MOSAIC tools. However, supporting common study processes requires implementing workflows that span different basic services, such as patient registration, followed by pseudonym generation and concluded by consent collection. To achieve this, an integration layer was developed to provide a unified Representational state transfer (REST) application programming interface (API) as a basis for more complex workflows. Based on this API, a unified graphical user interface was also implemented, providing an integrated view of information objects and workflows supported by the TTP. The API was implemented using Java and Spring Boot, while the graphical user interface was implemented in PHP and Laravel. Both services use a shared Keycloak instance as a unified management system for roles and rights. Results: By the end of 2022, the TTP has already supported more than 10 research projects since its launch in December 2019. Within these projects, more than 3000 identities were stored, more than 30,000 pseudonyms were generated, and more than 1500 consent forms were submitted. In total, more than 150 people regularly work with the software platform. By implementing the integration layer and the unified user interface, together with comprehensive roles and rights management, the effort for operating the TTP could be significantly reduced, as personnel of the supported research projects can use many functionalities independently. Conclusions: With the architecture and components described, we created a user-friendly and compliant environment for supporting research projects. We believe that the insights into the design and implementation of our TTP can help other institutions to efficiently and effectively set up corresponding structures.

Quantitative relationships among high-throughput sequencing, cyanobacteria toxigenic genotype abundance and microcystin occurrence in bathing waters.

Toxin-producing cyanobacteria pose significant threats to human and animal health if exposed during recreational activities in bathing waters. To better safeguard public health and reduce health risks during the bathing season, an effective monitoring and management strategy is required. Molecular tools used to monitor toxigenic cyanobacteria have been evaluated on the basis of the efficiency and applicability of the method used to (i) establish an early-warning monitoring strategy for EU bathing water sites using both targeted quantitative polymerase chain reaction (qPCR) and non-targeted high-throughput sequencing (HTS) genotype analysis and (ii) to compare the toxigenic potential of cyanobacteria with actual microcystin (MC) occurrence and concentrations. For this purpose, 16 bathing water sites were monitored according to the bathing water directive (BWD) of the European Union (EU) during the bathing season of the summer of 2020 in eastern Austria. The cyanobacterial community composition was analyzed through HTS and qPCR by targeting the microcystin synthetase B gene (mcyB), which indicates MC synthesis within the genera Microcystis and Planktothrix. Within the genus Microcystis, which was identified as the primary MC producer, the mcyB genotypes formed stable subpopulations that increased linearly in correlation with the total Microcystis population. Notably, the HTS cell equivalents assigned to Microcystis and Planktothrix correlated with the corresponding qPCR estimates of genotype abundance, which serves as a confirmation of the suitability of (semi)-quantitative sequencing through HTS. In addition to the elevated trophic state, reduced transparency, increasing water temperatures, as well as cyanobacterial HTS read numbers and Microcystis cell number equivalents per mL estimated through qPCR, were associated with positive MC samples. Therefore, in combination with the monitoring of standard environmental parameters, the use of HTS and qPCR techniques is considered highly useful to ensure the timely identification of health risks to recreational users, as mandated by the BWD.

Anonymization of whole slide images in histopathology for research and education.

Objective: The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods: We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results: Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions: Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

Telestroke ambulances in prehospital stroke management: concept and pilot feasibility study.

BACKGROUND AND PURPOSE: Pre- and intrahospital time delays are major concerns in acute stroke care. Telemedicine-equipped ambulances may improve time management and identify patients with stroke eligible for thrombolysis by an early prehospital stroke diagnosis. The aims of this study were (1) to develop a telestroke ambulance prototype; (2) to test the reliability of stroke severity assessment; and (3) to evaluate its feasibility in the prehospital emergency setting. METHODS: Mobil, real-time audio-video streaming telemedicine devices were implemented into advanced life support ambulances. Feasibility of telestroke ambulances and reliability of the National Institutes of Health Stroke Scale assessment were tested using current wireless cellular communication technology (third generation) in a prehospital stroke scenario. Two stroke actors were trained in simulation of differing right and left middle cerebral artery stroke syndromes. National Institutes of Health Stroke Scale assessment was performed by a hospital-based stroke physician by telemedicine, by an emergency physician guided by telemedicine, and "a posteriori" on the basis of video documentation. RESULTS: In 18 of 30 scenarios, National Institutes of Health Stroke Scale assessment could not be performed due to absence or loss of audio-video signal. In the remaining 12 completed scenarios, interrater agreement of National Institutes of Health Stroke Scale examination between ambulance and hospital and ambulance and "a posteriori" video evaluation was moderate to good with weighted κ values of 0.69 (95% CI, 0.51-0.87) and 0.79 (95% CI, 0.59-0.98), respectively. CONCLUSION: Prehospital telestroke examination was not at an acceptable level for clinical use, at least on the basis of the used technology. Further technical development is needed before telestroke is applicable for prehospital stroke management during patient transport.

EMPAIA App Interface: An open and vendor-neutral interface for AI applications in pathology.

BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. METHODS: The EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. RESULTS: Five commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. CONCLUSIONS: Existing AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

Diversity of West Nile and Usutu virus strains in mosquitoes at an international airport in Austria.

Increased globalization and international transportation have resulted in the inadvertent introduction of exotic mosquitoes and new mosquito-borne diseases. International airports are among the possible points of entry for mosquitoes and their pathogens. We established a mosquito and mosquito-borne diseases monitoring programme at the largest international airport in Austria and report the results for the first two years, 2018 and 2019. This included weekly monitoring and sampling of adult mosquitoes, and screening them for the presence of viral nucleic acids by standard molecular diagnostic techniques. Additionally, we surveyed the avian community at the airport, as birds are potentially amplifying hosts. In 2018, West Nile virus (WNV) was detected in 14 pools and Usutu virus (USUV) was detected in another 14 pools of mosquitoes (minimum infection rate [MIR] of 6.8 for each virus). Of these 28 pools, 26 consisted of female Culex pipiens/torrentium, and two contained male Culex sp. mosquitoes. Cx. pipiens/torrentium mosquitoes were the most frequently captured mosquito species at the airport. The detected WNV strains belonged to five sub-clusters within the sub-lineage 2d-1, and all detected USUV strains were grouped to at least seven sub-clusters among the cluster Europe 2; all strains were previously shown to be endemic in Austria. In 2019, all mosquito pools were negative for any viral nucleic acids tested. Our study suggests that airports may serve as foci of arbovirus activity, particularly during epidemic years, and should be considered when designing mosquito control and arbovirus monitoring programmes.

Hyalomma spp. in Austria-The Tick, the Climate, the Diseases and the Risk for Humans and Animals.

Recently, ticks of Hyalomma spp. have been found more often in areas previously lacking this tick species. Due to their important role as a vector of different diseases, such as Crimean-Congo-hemorrhagic fever (CCHF), the occurrence and potential spread of this tick species is of major concern. So far, eight Hyalomma sp. ticks were found between 2018 and 2021 in Austria. A serological investigation on antibodies against the CCHF virus in 897 cattle as indicator animals displayed no positive case. During observation of climatic factors, especially in the period from April to September, the year 2018 displayed an extraordinary event in terms of higher temperature and dryness. To estimate the risk for humans to come in contact with Hyalomma sp. in Austria, many parameters have to be considered, such as the resting place of birds, availability of large livestock hosts, climate, density of human population, etc.

Emergence of SARS-CoV-2 Alpha lineage and its correlation with quantitative wastewater-based epidemiology data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gave rise to an international public health emergency in 3 months after its emergence in Wuhan, China. Typically for an RNA virus, random mutations occur constantly leading to new lineages, incidental with a higher transmissibility. The highly infective alpha lineage, firstly discovered in the UK, led to elevated mortality and morbidity rates as a consequence of Covid-19, worldwide. Wastewater surveillance proved to be a powerful tool for early detection and subsequent monitoring of the dynamics of SARS-CoV-2 and its variants in a defined catchment. Using a combination of sequencing and RT-qPCR approaches, we investigated the total SARS-CoV-2 concentration and the emergence of the alpha lineage in wastewater samples in Vienna, Austria linking it to clinical data. Based on a non-linear regression model and occurrence of signature mutations, we conclude that the alpha variant was present in Vienna sewage samples already in December 2020, even one month before the first clinical case was officially confirmed and reported by the health authorities. This provides evidence that a well-designed wastewater monitoring approach can provide a fast snapshot and may detect the circulating lineages in wastewater weeks before they are detectable in the clinical samples. Furthermore, declining 14 days prevalence data with simultaneously increasing SARS-CoV-2 total concentration in wastewater indicate a different shedding behavior for the alpha variant. Overall, our results support wastewater surveillance to be a suitable approach to spot early circulating SARS-CoV-2 lineages based on whole genome sequencing and signature mutations analysis.

First Nationwide Monitoring Program for the Detection of Potentially Invasive Mosquito Species in Austria.

In Austria, only fragmented information on the occurrence of alien and potentially invasive mosquito species exists. The aim of this study is a nationwide overview on the situation of those mosquitoes in Austria. Using a nationwide uniform protocol for the first time, mosquito eggs were sampled with ovitraps at 45 locations in Austria at weekly intervals from May to October 2020. The sampled eggs were counted and the species were identified by genetic analysis. The Asian tiger mosquito Aedes albopictus was found at two sites, once in Tyrol, where this species has been reported before, and for the first time in the province of Lower Austria, at a motorway rest stop. The Asian bush mosquito Aedes japonicus was widespread in Austria. It was found in all provinces and was the most abundant species in the ovitraps by far. Aedes japonicus was more abundant in the South than in the North and more eggs were found in habitats with artificial surfaces than in (semi-) natural areas. Further, the number of Ae. japonicus eggs increased with higher ambient temperature and decreased with higher wind speed. The results of this study will contribute to a better estimation of the risk of mosquito-borne disease in Austria and will be a useful baseline for a future documentation of changes in the distribution of those species.

Real-time PCR for single-nucleotide polymorphism detection in the 16S rRNA gene as an indicator for extensive drug resistance in Mycobacterium tuberculosis.

OBJECTIVES: A real-time PCR screening system was established for rapid detection of single-nucleotide polymorphisms (SNPs) at positions 1401, 1402 and 1484 of the 16S rRNA gene of Mycobacterium tuberculosis leading to resistance to amikacin, kanamycin and capreomycin. Resistances to the respective drugs may indicate the presence of an extensively drug-resistant (XDR) strain of M. tuberculosis. METHODS: Fifty-seven M. tuberculosis isolates that tested phenotypically susceptible or resistant to amikacin, capreomycin or both were subjected to 1401-2/1484 real-time PCR to screen for SNPs in the respective rrs region. RESULTS: 1401-2 and 1484 wild-type and mutant M. tuberculosis strains displayed distinct melting peaks. Of the cross-resistant strains, 86.7% displayed A1401G SNPs, 76.9% of amikacin-resistant strains did not display rrs SNPs and one capreomycin-resistant strain showed a C1402T SNP. CONCLUSIONS: Phenotypic drug susceptibility testing takes several weeks, but with the 1401-2/1484 real-time PCR a preliminary diagnosis can be made within a few hours. SNPs in the rrs region are not exclusively involved in the development of resistances to amikacin and capreomycin. However, 80.0% of XDR-tuberculosis samples tested were detected with the real-time PCR screening assay of the present study.